99 Advanced Algorithms Handbook With Python
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99 Advanced Algorithms Handbook With Python
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Author : Jamie Flux
language : en
Publisher: Independently Published
Release Date : 2024-11-21
99 Advanced Algorithms Handbook With Python written by Jamie Flux and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-21 with Computers categories.
Immerse yourself in a comprehensive exploration of the most pivotal and sophisticated algorithms shaping the landscape of modern programming. This extensive compendium offers seasoned developers and computer scientists a definitive guide to 99 advanced algorithms, each meticulously selected for its significance and practical applications in the field. Structured for both learning and reference, each chapter focuses on a single algorithm, presenting: Concise Explanations: Understand the underlying principles and logic with clear, succinct descriptions that distill complex concepts into accessible insights. Practical Code Implementations: Access ready-to-use code snippets written in clean, efficient syntax, designed to integrate seamlessly into your projects and research. Applications and Use Cases: Discover how each algorithm applies to real-world problems across domains such as data compression, cryptography, machine learning, computational geometry, and network optimization. By bridging theory and practice, this compendium not only enhances your algorithmic repertoire but also sharpens your problem-solving skills. Whether optimizing existing systems or pioneering new solutions, you'll find the tools and knowledge to advance your work to new heights. Elevate your expertise and stay at the forefront of technological innovation with this essential resource-a must-have addition to the professional library of any serious programmer or academic.
99 Aerospace Engineering Algorithms Handbook With Python
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Author : Jamie Flux
language : en
Publisher: Independently Published
Release Date : 2024-11-21
99 Aerospace Engineering Algorithms Handbook With Python written by Jamie Flux and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-21 with Technology & Engineering categories.
Unlock new horizons in aerospace engineering with this comprehensive guide to advanced programming algorithms-all meticulously implemented in Python. Spanning 99 detailed chapters, this authoritative volume is an indispensable resource for engineers, researchers, and students aiming to propel their understanding and application of cutting-edge computational methods. Key Features: In-Depth Exploration of Complex Algorithms: Dive into sophisticated topics such as high-fidelity computational fluid dynamics, orbital mechanics, aeroelasticity, and hypersonic flow simulations. Each chapter provides a deep dive into a specific algorithm, complete with mathematical foundations and practical applications. Python Implementations: Benefit from fully functional Python code examples that bring each algorithm to life. Learn how to implement, customize, and optimize these algorithms for your own projects. Interdisciplinary Applications: Discover how advanced algorithms can be applied across various domains, including propulsion system modeling, guidance and control systems, machine learning in aerospace applications, and more. Real-World Problem Solving: Equip yourself with tools and techniques to address real-world aerospace challenges. From aerodynamic shape optimization to satellite attitude control, this book bridges the gap between theory and practice. Cutting-Edge Techniques: Stay ahead of the curve with the latest methodologies in adaptive control, computational aeroacoustics, multidisciplinary design optimization, and artificial intelligence applications in aerospace. Whether you're developing next-generation aircraft, designing intricate space missions, or conducting advanced research, this book provides the essential algorithms and programming techniques to advance your work. Harness the power of Python to drive innovation, optimize performance, and contribute to the future of aerospace engineering. Elevate your expertise and become a catalyst for innovation in the aerospace sector with this essential programming algorithm guide.
Advanced Algorithms And Data Structures
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Author : Marcello La Rocca
language : en
Publisher: Simon and Schuster
Release Date : 2021-08-10
Advanced Algorithms And Data Structures written by Marcello La Rocca and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-10 with Computers categories.
"An accessible introduction to the fundamental algorithms used to run the world." - Richard Vaughan, Purple Monkey Collective Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. Summary As a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Can you improve the speed and efficiency of your applications without investing in new hardware? Well, yes, you can: Innovations in algorithms and data structures have led to huge advances in application performance. Pick up this book to discover a collection of advanced algorithms that will make you a more effective developer. About the book Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You’ll discover cutting-edge approaches to a variety of tricky scenarios. You’ll even learn to design your own data structures for projects that require a custom solution. What's inside Build on basic data structures you already know Profile your algorithms to speed up application Store and query strings efficiently Distribute clustering algorithms with MapReduce Solve logistics problems using graphs and optimization algorithms About the reader For intermediate programmers. About the author Marcello La Rocca is a research scientist and a full-stack engineer. His focus is on optimization algorithms, genetic algorithms, machine learning, and quantum computing. Table of Contents 1 Introducing data structures PART 1 IMPROVING OVER BASIC DATA STRUCTURES 2 Improving priority queues: d-way heaps 3 Treaps: Using randomization to balance binary search trees 4 Bloom filters: Reducing the memory for tracking content 5 Disjoint sets: Sub-linear time processing 6 Trie, radix trie: Efficient string search 7 Use case: LRU cache PART 2 MULTIDEMENSIONAL QUERIES 8 Nearest neighbors search 9 K-d trees: Multidimensional data indexing 10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval 11 Applications of nearest neighbor search 12 Clustering 13 Parallel clustering: MapReduce and canopy clustering PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER 14 An introduction to graphs: Finding paths of minimum distance 15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections 16 Gradient descent: Optimization problems (not just) on graphs 17 Simulated annealing: Optimization beyond local minima 18 Genetic algorithms: Biologically inspired, fast-converging optimization
Python Programming Basic To Advance Programs With Solution
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Author : Dr. Jitendra Singh Kushwah, Mr. Shirish Mohan Dubey Mr. Mahendra Singh Yadav
language : en
Publisher: BFC Publications
Release Date : 2024-06-22
Python Programming Basic To Advance Programs With Solution written by Dr. Jitendra Singh Kushwah, Mr. Shirish Mohan Dubey Mr. Mahendra Singh Yadav and has been published by BFC Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-22 with Computers categories.
Advanced Data Analytics Using Python
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Author : Sayan Mukhopadhyay
language : en
Publisher: Apress
Release Date : 2018-03-29
Advanced Data Analytics Using Python written by Sayan Mukhopadhyay and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-29 with Computers categories.
Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. What You Will Learn Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP Who This Book Is For Data scientists and software developers interested in the field of data analytics.
Handbook Of Metaheuristic Algorithms
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Author : Chun-Wei Tsai
language : en
Publisher: Elsevier
Release Date : 2023-05-30
Handbook Of Metaheuristic Algorithms written by Chun-Wei Tsai and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-30 with Computers categories.
Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains. Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems. - Presents a unified framework for metaheuristics and describes well-known algorithms and their variants - Introduces fundamentals and advanced topics for solving engineering optimization problems, e.g., scheduling problems, sensors deployment problems, and clustering problems - Includes source code based on the unified framework for metaheuristics used as examples to show how TS, SA, GA, ACO, PSO, DE, parallel metaheuristic algorithm, hybrid metaheuristic, local search, and other advanced technologies are realized in programming languages such as C++ and Python
Advanced Data Science And Analytics With Python
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Author : Jesus Rogel-Salazar
language : en
Publisher: CRC Press
Release Date : 2020-05-05
Advanced Data Science And Analytics With Python written by Jesus Rogel-Salazar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-05 with Business & Economics categories.
Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app. About the Author Dr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.
Advanced Applied Deep Learning
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Author : Umberto Michelucci
language : en
Publisher: Apress
Release Date : 2019-09-28
Advanced Applied Deep Learning written by Umberto Michelucci and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-28 with Computers categories.
Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level. What You Will Learn See how convolutional neural networks and object detection work Save weights and models on disk Pause training and restart it at a later stage Use hardware acceleration (GPUs) in your code Work with the Dataset TensorFlow abstraction and use pre-trained models and transfer learning Remove and add layers to pre-trained networks to adapt them to your specific project Apply pre-trained models such as Alexnet and VGG16 to new datasets Who This Book Is For Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.
99 Nuclear Engineering Algorithms Handbook With Python
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Author : Jamie Flux
language : en
Publisher: Independently Published
Release Date : 2024-11-22
99 Nuclear Engineering Algorithms Handbook With Python written by Jamie Flux and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-22 with Technology & Engineering categories.
A Comprehensive Guide to Advanced Programming Algorithms in Nuclear Engineering Delve deep into the computational backbone of nuclear engineering with this exhaustive compendium of advanced programming algorithms. This authoritative resource presents a meticulously curated collection of 99 sophisticated algorithms, each dedicated to solving complex problems in nuclear science and engineering. Key Features: In-Depth Algorithm Exploration: Each chapter focuses on a specific algorithm, providing detailed explanations, mathematical formulations, and practical implementation strategies. Wide-Ranging Applications: Covers a spectrum of topics including Monte Carlo neutron transport, discrete ordinates methods, thermal hydraulics, reactor kinetics, fuel cycle optimization, and multiphysics coupling. Practical Implementation: Emphasizes real-world applications with code snippets, pseudocode, and step-by-step guides to facilitate understanding and implementation. Advanced Computational Techniques: Explores high-performance computing, parallel processing, adaptive mesh refinement, and machine learning applications tailored for nuclear engineering challenges. Cutting-Edge Topics: Includes contemporary methods such as hybrid deterministic-stochastic simulations, uncertainty quantification, adjoint methods, and reduced-order modeling. Who Should Read This Book: Nuclear Engineers and Scientists: Professionals seeking advanced computational tools to address complex nuclear systems and reactor designs. Computational Physicists and Engineers: Those interested in the application of numerical methods and algorithms in the nuclear domain. Graduate Students and Researchers: An invaluable reference for advanced studies and research in nuclear engineering and computational methods. Software Developers in Scientific Computing: Programmers developing simulation software for nuclear applications who require a deep understanding of underlying algorithms. Benefits: Enhance Computational Skills: Develop proficiency in implementing advanced algorithms crucial for modern nuclear engineering tasks. Stay Ahead in the Field: Keep abreast of the latest computational techniques and methodologies driving innovation in nuclear science. Problem-Solving Resource: A go-to reference for tackling computational challenges in reactor physics, thermal analysis, fuel management, and safety assessments. About the Content: Structured to provide both breadth and depth, the book allows readers to: Understand Fundamental Concepts: Begin with foundational algorithms before progressing to more complex methods. Apply Algorithms Practically: Each chapter includes practical examples, facilitating the transition from theory to application. Adapt and Innovate: Gain insights into how algorithms can be adapted to specific problems or used as a foundation for developing new methods. Elevate your computational capabilities in nuclear engineering with this definitive guide to advanced programming algorithms.
Python Programming Guide For Gcse Computer Science Includes Python Files
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Author : CGP Books
language : en
Publisher: CGP Ltd
Release Date : 2022-04-13
Python Programming Guide For Gcse Computer Science Includes Python Files written by CGP Books and has been published by CGP Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-13 with Young Adult Nonfiction categories.
This brilliant CGP book is the perfect no-nonsense guide for anyone who wants to learn Python! It contains clear notes on all the key programming skills — ideal for Python beginners, GCSE Computer Science students, and as an introduction to Python at A-Level. There are plenty of useful practice questions, fully-worked examples and coding challenges throughout, with full answers included at the back of the book. We've even added over 250 downloadable files covering different areas of Python — they're just the ticket for anyone looking for practical, on-screen practice.